3 papers
cs.LG2026
ZO-Act: Efficient Zeroth-Order Fine-Tuning via One-Shot Activation-Informed Low-Rank Subspaces
Xun Dong, Yibo Xu, Naigang Wang +3
Zeroth-order (ZO) optimization enables fine-tuning large language models when backpropagation is unavailable or memory-prohibitive, but existing methods often perturb full model we…
cs.AI2026
FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory
Lei Wei, Xiao Peng, Xu Dong +2
Large language models deployed as autonomous agents face critical memory limitations, lacking selective forgetting mechanisms that lead to either catastrophic forgetting at context…
cs.LG2025
DiaBlo: Diagonal Blocks Are Sufficient For Finetuning
Selcuk Gurses, Aozhong Zhang, Yanxia Deng +5
Fine-tuning is a critical step for adapting large language models (LLMs) to domain-specific downstream tasks. To mitigate the substantial computational and memory costs of full-mod…